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Instrument Development for the FocaL Adult Gambling
Screen (FLAGS-EGM): A Measurement of Risk and Problem
Gambling Associated with Electronic Gambling Machines
Tony Schellinck,
1,2
Tracy Schrans,
2
Heather Schellinck,
1
& Michael Bliemel
1
1
Dalhousie University, Halifax, Nova Scotia, Canada.
2
Focal Research Consultants Limited, Halifax, Nova Scotia, Canada.
Abstract
Previous research, based on a survey of 374 electronic machine gamblers living
in Ontario, Canada, led to the selection of statements and the creation of
ten constructs for the development of a new instrument, the FocaL Adult
Gambling Screen for Electronic Gambling Machines (FLAGS-EGM). In this
study, we used the Partial Least Squares Path Analysis form of Structural
Equation Modelling to produce a hierarchical set of the ten constructs with proven
predictive power for problem gambling. Receiver Operating Characteristic
analysis identified cut off values for all of the constructs that predicted the target
values with the desired degree of accuracy. Active gamblers were placed in five
categories: No Detectable Risk, Early Risk, Intermediate Risk, Advanced Risk
and Problem Gamblers. As described here, the FLAGS-EGM instrument has the
potential to be applied in many situations in which identification of at-risk EGM
gamblers is needed.
Résumé
Des recherches fondées sur une enquête menée auprès de 374 joueurs de jeux de
hasard électronique ont conduit à la sélection d’énoncés et à la création de dix
construits destinés à la mise au point d’un nouvel instrument appelé FocaL Adult
Gambling Screen for Electronic Gambling Machines (FLAGS-EGM). Nous avons eu
recours à une analyse des pistes causales par la technique des moindres carrés, une
forme d’analyse des équations structurelles, en vue de produire un ensemble
hiérarchique constitué des dix constructs ayant démontré une efficacité prédictive
relativement aux problèmes de jeu. Une analyse de la fonction d’efficacité du
récepteur a permis de définir des valeurs seuils pour tous les constructs ayant prédit
des valeurs cibles avec le degré de précision anticipé. Les joueurs actifs ont été
répartis en cinq catégories : aucun risque détectable, risque précoce, risque
intermédiaire, risque accru et joueur compulsif. L’instrument FLAGS-EGM
174
Journal of Gambling Issues
Issue 30, May 2015 DOI: http://dx.doi.org/10.4309/jgi.2015.30.8
http://igi.camh.net/doi/pdf/10.4309/jgi.2015.30.8
pourrait s’appliquer à un grand nombre de situations où il est nécessaire d’identifier
les joueurs à risque parmi ceux qui s’adonnent aux jeux de hasard électronique.
Introduction
Few gambling assessment screens have been specifically designed to identify an
individual’s risk for harmful consequences prior to the onset of actual problems. It
was our objective, in developing the FLAGS-EGM, to create such an instrument.
The FLAGS-EGM also categorized individuals as problem gamblers, although this
was not in fact its main purpose. We also wanted to design a screen that could be
self-administered. During the extensive development phase of this research
(Schellinck, T., Schrans, Schellinck, H., & Bliemel, in press) we focused on ensuring
the statements considered for inclusion in the instrument were clearly understood
and consistently interpreted by gamblers (Appendix). Ideally, this measure would
educate and alert individuals regarding the likelihood of their risk of becoming
problem gamblers and motivate them to adopt behaviours that would reduce their
chances of experiencing harms.
Maddern and Rogala (2006) administered a 36-statement pilot version of the
instrument to a sample of at-risk gamblers. These individuals found the statements
were easy to understand and were an accurate assessment of their beliefs and
behaviours. Many subjects indicated that it would motivate them to change their
behaviours in regards to gambling. Buckley (2013) found that administering the
FLAGS-EGM to a sample of gamblers and then providing them with their
indicators of risk and classification of risk significantly increased their readiness to
change their gambling behaviour.
We tested the validity and reliability of the five reflective and five formative constructs in
the FLAGS-EGM (Table 1) as a further step in developing the instrument (Schellinck
T. et al., in press). In the current study, we modelled the relationships among these
constructs to determine the nature and timing of their influence in the evolution of a
problem gambler. Constructs found to be significantly positioned along the path to
problem gambling were used to create indicators of risk. If any of the variables were
hierarchical in nature, those variables found to precede other constructs were considered
to be earlier indicators of risk. Using Receiver Operating Characteristic (ROC) analysis
(Metz, 2006) we examined the predictive nature of the constructs to establish cut offs
such that individuals who scored at or above the designated level would be considered at
risk. Those constructs found to be directly connected to the problem gambling
constructs were considered indicative of the most advanced level of risk.
As described in Schellinck, T. et al. (in press) the new instrument was based on a
research model previously created to identify antecedents of problem gambling as
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
well as on an extensive review of the literature. To create the instrument structure
and scoring system we needed to complete the following steps:
Demonstrate that the construct scores were related to harms due to gambling,
Establish a hierarchy to the constructs in terms of when they would be manifested
prior to the gambler becoming a problem gambler,
Determine sum score levels for each construct that would accurately provide an
indication that a person can be characterised as at risk by this construct, and
Set criteria by which these indications would assign gamblers to various levels of risk.
Partial Least Squares Path Analysis form of Structural Equation Modelling (PLS-
SEM) (Chin, 1998; Chin & Newsted, 1999; Hair, Ringle, & Sarstedt, 2011) was used
to achieve steps 1, 2 and 4. We chose this method as PLS-SEM has become the
common one of investigating in the area of management research the cause-effect
relations between latent constructs. It maximises the explained variance of the
dependent latent constructs similar to multiple regression analysis. In particular,
when the goal of the model development process is prediction and theory
development, as it was here, PLS-SEM is viewed as the most appropriate method
of analysis (Hair et al., 2011). PLS-SEM provides many advantages over standard
Structural Equation Modelling. It identifies key driver constructs when predicating a
target construct, can easily accommodate both formative and reflective constructs, is
used for exploratory research into structural theory, and is suitable for use in a
complex structural model with many constructs and indicators. Moreover, PLS-
SEM can be used with a relatively small sample. The analysis is built on the
properties of Ordinary Least Squares (OLS) regression which means that traditional
methods of estimating the sample power as outlined by Cohen (1992) can be used
(Hair, Hult, Ringle, & Sarstedt, 2013). Our largest construct (Negative Con-
sequences) had 14 items which, extrapolating from figures presented by Hair et al.
(2013, p. 21), indicated that a sample size of 293 or larger would provide a power of
80% or better, a minimum R
2
of 0.1 and a 1% significance level. Our sample size of
374 was clearly adequate for the required analysis.
Table 1
Construct Type and Number of Statements in each FLAGS-EGM Construct
Construct Construct Type Number of Statements
Erroneous Cognitions Beliefs Formative 5
Erroneous Cognitions Motives Formative 4
Preoccupation Desire Reflective 4
Preoccupation Obsession Reflective 2
Risky Behaviours Earlier Formative 6
Risky Behaviours Later Formative 6
Impaired Control Continue Play Reflective 5
Impaired Control Begin Play Reflective 3
Negative Consequences Formative 14
Persistence Reflective 4
FLAGS-EGM Instrument (Beta): Total Statements 53
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
To establish accurate sum score levels for each construct (step 3, above) ROC
analysis, using each construct as a predictor of an appropriate target variable, was
required. In this case, ROC analysis was used to assign cut off points on the summed
scores to provide indicators of risk for respondents. Combinations of these indicators
were used to allocate electronic machine gamblers to risk categories.
Method
Information obtained from a sample of regular EGM gamblers who played ‘‘ the slots’’
on average at least once a month over the previous year was used to develop the
instrument. Over a five-day period, potential respondents were asked to participate in a
research panel as they entered a casino in Ontario, Canada. Telephone interviews were
conducted in April and May of 2009 with the sample of panel members. A total of 422
surveys were completed out of 610 eligible panel members (69.2%), with 48 disqualified
because of respondent selection criteria (e.g., played slots less than once per month over
the previous year), leaving 374 completed surveys available for analysis. This sample
comprised 150 males (40.1%) and 224 females (59.9%); the median age was 63 with ages
ranging from 23 to 89. The first language of 85.5% of the participants was English, 46.8%
were retired and 2.1% were unemployed.
The participants indicated they had never received any treatment or services for substance
use or gambling or mental health issues. Slightly over half of participants (53.5%)
indicated they gambled weekly or daily on the slots. 70.1% also purchased lottery tickets
at least once a month, 2.4% had participated in Internet gambling in the last year, 2.1%
played casino table games monthly, 10.6% played card games for money monthly, 11.5%
went played bingo in bingo halls monthly, and 7.2% gambled on horse racing monthly.
The survey comprised 132 dichotomous statements that were randomized for each
participant to reduce the risk of common method bias (Bliemel & Hassanein, 2007).
The survey also gathered demographic information, general gambling behaviour and
playing patterns. Briefly, the statements were formed into a set of five formative and
five reflective constructs, with 53 statements, for inclusion in the FLAGS-EGM
instrument. The specific process through which the statements were formed, and the
logic underlying the process, is described in Schellinck, T. et al. (in press).
PLS-SEM Analysis
Four criteria were established by Urbach and Ahlemann (2010) that can be used to
evaluate the validity of the SEM-PLS model derived using the ten constructs:
Coefficients of determination (R
2
) where values of 0.670, 0.333 and 0.190 were
considered substantial, moderate and weak respectively.
Significant path coefficients using bootstrapping with 5000 runs.
Independent latent variables having substantial impact on dependent latent values (f
2
)
with values of 0.35, 0.15 and 0.02 considered to be large, medium and low effect levels.
Predictive Relevance (Q
2
) where the threshold for significant impact was 40.
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
Development of Risk and Problem Gambling Categories
To classify gamblers into risk categories we grouped the constructs based on the
following six criteria: 1. The constructs were ordered based on the hypothesized
direction of causality confirmed by the strongest predictive relationships found in the
PLS-SEM model. 2. Constructs in the PLS-SEM model needed to be directly
connected to constructs in the next highest level of risk/problem gambler. 3. The
latter constructs needed to have a major impact (f
2
) on the higher risk/problem
gambler constructs. 4. Higher risk/problem gambler constructs should be influenced
by lower risk constructs. 5. Cognitive-based constructs—such as risky beliefs and
motives, when placed at the beginning of the PLS-SEM model—were designated
early indicators and grouped accordingly. 6. If a gambler exhibited behavioural
based indications (i.e., Impaired Control and Risky Practices) they were classified at
a more advanced risk level (i.e., Intermediate or Advanced Risk levels). Once
gamblers were engaged in risky behaviours they were considered to be at a more
advanced stage in the progression towards becoming a Problem Gambler.
It should be noted that three of the constructs were split into two parts during the
construct development phase of the study: Preoccupation Desire and Preoccupation
Obsession, Impaired Control Continue and Impaired Control Begin, and Risky
Practices Earlier and Risky Practices Later (Schellinck, T. et al., in press). Each resulting
pair had an earlier and later risk construct that could be inferred based on the frequency
of responses to the statements and their ultimate positioning in the PLS-SEM model.
Consequently, the decision was made to place those gamblers who had an indication of
risk on the later risk constructs into the Advanced Risk category.
ROC Analysis
The two criteria used to assign indicator cut offs for each of the ten constructs were
sensitivity (true positive rate) and 1 minus (-) specificity (false negative rate) as determined
by ROC analysis (Metz, 2006). The accuracy of a predictor variable or model, in
correctly classifying a person (the target value in the target dichotomous state variable),
could be assessed over the range of the predictor variable’s values. For each possible
value of the predictor variable a classification matrix was produced and the sensitivity,
specificity and the chi-square statistic for the matrix calculated. ROC analysis used these
values to produce a graph of the ROC curve based on sensitivity and 1- specificity such
that the power of the model to classify gamblers could be assessed visually.
As a diagonal line in the graph indicates a performance level no better than chance, the
greater the separation of the ROC curve from the diagonal the better the model’s
performance. The degree of separation is summarized by the total area under the curve;
the closer to 100% area coverage under the curve, the better the model performance. The
ROC analysis also produces an overall significance test. As Conigrave, Hall, and
Saunders (1995) recommended, the predictor variable indicator value was selected at the
point in the ROC curve that corresponded with the maximized chi-square test score.
This approach weighted the sensitivity and specificity equally.
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
The state variable used depended on the construct being evaluated. As FLAGS-
EGM is a hierarchical model, we did not expect the constructs at the beginning of the
hierarchy (i.e., Risky Beliefs and Motives) to predict the target value at the end of the
hierarchy (i.e., problem gambling) accurately. Negative Consequences and
Persistence were evaluated using a score of 8+on the PGSI as the state value.
The PGSI Problem Gambler category was chosen as the value because it is
commonly used to identify problem gamblers and because it has been shown to have
considerable convergent validity with other instruments, such as the DSM-IV (Ferris
& Wynne, 2001). The other constructs were evaluated using the indicators in the
higher levels of risk as the target values. Specifically, the FLAGS-EGM Problem
Gambler indicator was used as the target variable for Preoccupation Obsession,
Impaired Control Begin and Risky Practices Later. The FLAGS Advanced Risk
indicator was used for Risky Practices Earlier and Impaired Control Continue. The
FLAGS-EGM Intermediate Risk indicator was used for Preoccupation Desire,
Risky Cognitions Motives and Risky Cognitions Beliefs.
Comparison to PGSI
A modified version of the Problem Gambling Severity Index (PGSI) component of
the Canadian Problem Gambling Index (Ferris & Wynne, 2001) was administered
during the interview to provide a measure of problem gambling status and to assess
concordance in categorizing EGM gamblers as at risk or Problem Gamblers with the
new instrument. The statements in the PGSI were modified (see Table 8 for the
modified statements) by specifically referencing slot play and casino play as the form
of gambling indicated in the statements. This change ensured that the two
instruments would be referencing the same behaviour when it came to identifying
sources of the risk or problem gambling status. To compare the success of PGSI and
FLAGS-EGM in this context we created two dichotomous variables that identified
problem gamblers in each of the instruments and which produced a tetrachoric
correlation as a measure of concordance. The tetrachoric correlation is considered
the appropriate statistic, rather than Pearson or Spearman correlations or the kappa
statistic, when comparing two categories (Bonett & Price, 2005; Uebersax, 1987).
As the PGSI has two risk categories and the FLAGS-EGM has three, we needed to
combine two of the FLAGS-EGM categories together to compare the risk
classifications between the two instruments. The PGSI uses a fairly wide range of
scores (3–7) to assign gamblers to its Medium Risk category. Consequently, for
comparison purposes, we combined the Intermediate and Advanced Risk categories
of the FLAGS-EGM into a single category equivalent to the PGSI Medium Risk
category. As some of the gamblers could fall into either the No Detectable Risk/No Risk
or the Problem Gambler categories at either end of the scale for both instruments, in this
instance it would not be appropriate to use the tetrachoric correlation. We measured
overlap by taking the sample of all gamblers identified as at risk by either instrument
and determined the percent of common assignment to risk level.
179
INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
To aid in interpreting any discrepancies found between the classification by the two
instruments we created four discrepancy segments, PGSI at Low Risk or Medium Risk
but FLAGS-EGM No Detectable Risk, PGSI No Risk but FLAGS-EGM at Early
Risk or higher, PGSI Low Risk but FLAGS-EGM Intermediate Risk or higher, and
PGSI Medium Risk and FLAGS-EGM Problem Gamblers. Each segment was
compared on the ten FLAGS-EGM constructs and the nine PGSI statements. Author
judgment was used to interpret the results.
Results
Risk Levels Based on Partial Least Squares Analysis
Using SmartPLS the analysis started with a saturated model using all ten constructs,
all constructs connected, and then, non-significant paths removed. The direction of
the significant paths was then reversed, one path at a time, to ensure the largest
coefficients occurred when the path was in the expected direction. The resulting
eighteen paths were all significant at the p o0.05 level based on t-scores derived
from 5,000 bootstrapping runs (Figure 1).
Figure 1. PLS Model Showing Path Coefficients, T-Scores and Variance Explained in
Each Construct
180
INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
The variance explained (R
2
) was .633 for Negative Consequences and .718 for
Persistence, which puts the model into the substantial variance explained range.
Similarly, the variance explained for Risky Practices Earlier and Risky Practices
Later was .636 and .613 respectively.
The relative effect size (f
2
) for each preceding construct on the target construct (listed
at the top of the column) (Chin, 1998) is presented in Table 2. All of the effect levels
were above, or near, the medium level of 0.15 suggested by Urbach and Ahlemann
(2010). Risky Practices Later was strongly influenced by Risky Cognitions Motives
(0.47), while Negative Consequences was strongly influenced by Impaired Control
Begin (0.54) and Risky Practices Later (0.49). Negative Consequences (0.57) had the
most effect on Persistence. All ten constructs met the criterion for predictive
relevance (Q
2
) of values greater than zero, as shown in Table 3.
Classifying Gamblers into Five Categories
Gamblers who had an indication of both Negative Consequences and Persistence
were placed into the Problem Gambler category. As a result of the path analysis,
three constructs—Impaired Control Begin, Risky Practices Later and Preoccupation
Obsession—were designated as Advanced Risk indicators based on two criteria:
Each was found to lead directly into one of the problem gambling constructs of
Negative Consequences or Persistence, and each was significantly influential on
either Negative Consequences or Persistence (Figure 1). Two constructs were
designated as Intermediate Risk indicators: Impaired Control Continue and Risky
Practices Earlier. Both led to Advanced Risk constructs and were linked in the
PLS-SEM model progressively to lower risk constructs.
Three constructs were used to identify Early Risk gamblers. Risky Cognitions
Beliefswasfoundonlytoinfluence Risky Cognitions Motivesandwaspositioned
at the very start of the path with no other constructs influencing it. Its overall effect
on either Negative Consequences or Persistence was lowest of all constructs, at 0.16
Table 2
Effect Size (f
2
) of Constructs on Selected Target Constructs
Risky Practices Later Negative Consequences Persistence
Risky Cognitions Beliefs 0.20 0.16 0.19
Risky Cognitions Motives 0.47 0.33 0.38
Preoccupation Desire 0.29 0.28 0.30
Risky Practices Earlier 0.29 0.14 0.28
Impaired Control Continue 0.32 0.34 0.32
Preoccupation Obsessed 0.32 0.25 0.36
Impaired Control Begin 0.34 0.54 0.34
Risky Practices Later 0.49 0.28
Negative Consequences 0.57
Note. Values of 0.35, 0.15 and 0.02 are considered to be large, medium and low effect levels.
181
INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
and 0.19 respectively. As Risky Cognitions Motives is a formative construct and
located in the PLS-SEM model, where it affects both earlier and later constructs,
we used this construct to identify those gamblers who were earlier in the hierarchy
of risk for problem gambling. These individuals have yet to exhibit risky practices
or impaired control and yet had indications of risky cognitions. The intent was to
use this construct to identify persons who have a clear indication of risk before they
are gambling in a risky manner. Preoccupation Desire had fairly large levels of
influence (0.28–0.30) on Negative Consequences and Persistence. As this construct
followed Impaired Control Motives and was found to influence Impaired Control
Continue and Preoccupation Obsession, it was located early on the path to problem
gambling.
Table 4 summarizes the criteria used for classifying machine gamblers to one of the
five levels of risk for problem gambling.
Setting Criterion Levels for Constructs as Indicators
For all ten constructs analyzed using ROC analysis the statistical significance for the
models was po0.000. The results are summarized in Table 5. Sensitivity ranged
from 41.4% to 90.5%. Preoccupation Obsession and Risky Cognitions Motives had
sensitivities of 41.1% and 43.1% respectively; Negative Consequences and Impaired
Control Continue had sensitivities of 90.5% and 83.1% respectively. Specificity
ranged from 81.1% to 99.0%, with Risky Cognitions Beliefs scoring the lowest and
Preoccupation Obsession scoring the highest.
Six of the constructs formed indicators based on cut offs of two: Persistence,
Preoccupation Obsession, Impaired Control Begin, Risky Practices Later, Risky
Cognitions Motives and Risky Cognitions Beliefs. The four constructs with cut offs
of three were Negative Consequences, Risky Practices Earlier, Impaired Control
Continue and Preoccupation Desire.
Table 3
Predictive Relevance of Latent Variables for Persistence
Predictive Relevance (Q
2
)
Risky Cognitions Beliefs 0.294
Risky Cognitions Motives 0.122
Preoccupation Desire 0.091
Risky Practices Earlier 0.246
Impaired Control Continue 0. 285
Preoccupation Obsessed 0.249
Impaired Control Begin 0.314
Risky Practices Later 0.315
Negative Consequences 0.247
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
A Profile of Indications for Gamblers at Each of the Five Risk Levels
Table 6 presents the percentage of respondents with indications of risk for the total
sample as well as for the five FLAGS-EGM risk categories. For this particular
sample, the most prevalent risk indicator was Impaired Control Continue at 23.5%,
followed by three indicators with a similar prevalence: Preoccupation Desire
(18.4%), Risky Practices Earlier (17.9%) and Risky Cognitions Motives (17.1%).
Risky Practices Later (12.8%), Risky Cognitions Beliefs (8.8%) and Impaired
Control Begin (8.6%) had a relatively low prevalence in this sample. Only 3.7% of the
sample had an indication of Preoccupation Obsession.
Problem Gamblers. As shown in Table 6, individuals classified as Problem
Gamblers because of indications of both Negative Consequences and Persistence
Table 4
FLAGS Five Levels of Player Risk for Machine Gambling
Risk
Level
Label Description
Level V Problem
Gambler
A Problem Gambler is a person who flagged as exhibiting both Negative
Consequences and Persistence and is characterized as having experienced
harm in association with gambling yet is persisting in gambling.
Level IV Advanced
Risk
Those persons at Advanced Risk are not flagging as a Problem Gamblers
(i.e., scoring on Negative Consequence and Persistence) but hold one or
more indications on the five constructs directly connected to either
Negative Consequences or Persistence. Three of these constructs are
Impaired Control Begin, Preoccupation Obsessed and Risky Practices
Later. Negative Consequences and Persistence are included as it is
possible that a person only flagged on one of these constructs and,
therefore, has not (yet) reached the threshold for identification as a
problem gambler.
Level III Intermediate
Risk
Those at Intermediate Risk are not Problem or Advanced Risk gamblers,
but have been flagged on one or more of the Intermediate Risk constructs.
The Intermediate Risk constructs are Impaired Control Continue and
Risky Practices Earlier. Intermediate Risk Gamblers are not triggering on
Negative Consequences or exhibiting signs of Persistence. While higher in
the risk hierarchy than the Early Risk Gamblers these players comprise
individuals at pre-harm risk levels.
Level II Early Risk Those at Early Risk have flagged on at least one of Risky Cognitions
Beliefs, Risky Cognitions Motives or Preoccupation Desire but are not
triggering the Advanced Risk or Problem Gambling constructs and are
also characterized as a pre-harm risk group.
Level I No Indication
of Risk
Those at No Indication of Risk do not flag on any of the risk indicators
although it is possible that they answered yes to one or more statements
making up some of the constructs. For those subjects who answered yes
to at least one statement there was insufficient certainty for us to say
there was an indication on one of the dimensions.
Level 0 Non-Gambler A Non-Gambler is at no-risk currently because he or she is not now
engaging in behaviours that could lead to harm.
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
all had an indication of Impaired ControlContinue(100%).Theywerealsolikely
to have indications of Risky Practices Earlier (89.7%), Risky Practices Later
(82.8%) and Risky Cognitions Motives (82.8%). A large proportion of the
Problem Gamblers also had indications of Impaired Control Begin (65.5%) and
Preoccupation Desire (62.1%). Preoccupation Obsession (41.1%) and Risky
Cognitions Beliefs (31.0%) were common, but not frequent indicators for
Problem Gamblers.
Advanced Risk. Some of those categorized as Advanced Risk had indications of
Persistence (33.3%) or Negative Consequences (22.1%), but not both, as this would
have categorized them as Problem Gamblers. The most common indicators of risk
for this category were Impaired Control Continue (69.4%), Risky Practices Later
(66.7%), Risky Practices Earlier (55.6%) and Preoccupation Desire (55.6%). Both
Risky Cognitions Motives (38.9%) and Impaired Control Begin (36.1%) were fairly
prevalent among the Advanced Risk Gamblers, while Risky Cognitions Beliefs
(22.2%) and Preoccupation Obsession (14.3%) were less prevalent.
Intermediate Risk. Impaired Control Continue was the most prevalent indicator
(77.3%) for those in the Intermediate Risk category. Also, somewhat important were
Risky Practices Earlier (47.7%) and Preoccupation Desire (45.5%). Both Risky
Table 5
Results of ROC Analysis for Ten FLAGS-EGM Constructs
Construct State Variable Value Cut Off
Chosen
%
Indicated
Sensitivity Specificity Area
Under
Curve
Persistence PGSI 8+2 11.0% 76.2% 97.2% 95.0%
Negative
Consequences
PGSI 8+3 9.9% 90.5% 94.9% 95.6%
Preoccupation
Obsession
FLAGS-EGM PG 2 3.7% 41.4% 99.04% 78.1%
Impaired Control
Begin
FLAGS-EGM PG 2 8.6% 65.5% 96.2% 91.4%
Risky Practices
Later
FLAGS-EGM PG 2 12.8% 82.8% 93.0% 95.0%
Risky Practices
Earlier
FLAGS-EGM
Advanced Risk
3 17.9% 70.8% 93.2% 89.0%
Impaired Control
Continue
FLAGS-EGM
Advanced Risk
3 23.5% 83.1% 89.0% 89.8%
Preoccupation
Desire
FLAGS-EGM
Intermediate Risk
3 18.4% 53.2% 95.8% 82.7%
Risky Cognitions
Motives
FLAGS-EGM
Intermediate Risk
2 17.1% 43.1% 93.6% 75.7%
Risky Cognitions:
Beliefs
FLAGS Intermediate
Risk
2 27.3% 47.7% 81.1% 68.0%
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
Cognitions Motives (20.5%) and Risky Cognitions Beliefs (4.5%) have low
prevalence among those in this category.
Early Risk. For Early Risk Gamblers, the key indicators were Risky Cognitions
Motives (43.6%), followed by Risky Cognitions Beliefs (35.9%) and Preoccupation
Desire (28.2%).
Comparison of FLAGS-EGM to the PGSI
The overall distribution by risk categories was somewhat similar for the two
measures (Table 7). FLAGS-EGM identified 60.4% as No Detectable Risk, 10.4% as
Early Risk, 11.8% as Intermediate Risk, 9.6% as Advanced Risk and 7.8% as
Problem Gamblers. The PGSI identified 54.8% as No Risk, 19.3% as Low Risk,
20.3% as Medium Risk and 5.6% as Problem Gamblers. The PGSI found 39.6% of
the sample to be at risk compared to 31.8% for the FLAGS-EGM.
Comparison of the classification Problem Gambler by the two instruments produced
a tetrachoric correlation of 0.947, indicating a very high degree of agreement
between the two instruments in terms of identifying problem gamblers. Using the
Table 6
Percent of FLAGS-EGM Risk/PG Segments with Specific Indications of Risk
Constructs All Gamblers
N = 374
No Risk
N = 226
Early Risk
N=39
Intermediate
Risk N = 44
Advanced
Risk N = 36
Problem
Gambler
N=29
Persistence 11.0% 0.0% 0.0% 0.0% 33.3% 100%
Negative
Consequences
9.9% 0.0% 0.0% 0.0% 22.2% 100%
Preoccupation
Obsession
3.7% 0.0% 0.0% 0.0% 14.3% 41.4%
Impaired
Control Begin
8.6% 0.0% 0.0% 0.0% 36.1% 65.5%
Risky Practices
Later
12.8% 0.0% 0.0% 0.0% 66.7% 82.8%
Impaired
Control
Continue
23.5% 0.0% 0.0% 77.3% 69.4% 100.0%
Risky Practices
Earlier
17.9% 0.0% 0.0% 47.7% 55.6% 89.7%
Preoccupation
Desire
18.4% 0.0% 28.2% 45.5% 55.6% 62.1%
Risky
Cognitions
Motives
17.1% 0.0% 43.6% 20.5% 38.9% 82.8%
Risky
Cognitions
Beliefs
8.8% 0.0% 35.9% 4.5% 22.2% 31.0%
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
PGSI as the ‘‘ gold standard’’ for categorizing an individual as a Problem
Gambler, the sensitivity of the FLAGS-EGM measure was 85.7%, while the
specificity was 96.9%. The PGSI identified 21 problem gamblers in the sample
of 374 while the FLAGS-EGM identified 29. Eleven (37.9%) of those identified
as problem gamblers by the FLAGS-EGM were not categorized as such by
the PGSI. Only three individuals were categorized as problem gamblers by
the PGSI and not by the FLAGS-EGM. This particular result produced an
overlap of only 56.2% subjects being identified as a Problem Gambler by both
instruments.
The overlap of those gamblers categorized at any level of risk by either instrument
(with the FLAGS-EGM Intermediate Risk and Advanced Risk categories
combined) was 33.9%. The greatest source of discrepancy between the two measures
occurred when a gambler was categorized as At Risk by one instrument and at No
Risk or No Detectable Risk by the other. This particular inconsistency happened in
21.1% of the cases.
Table 8 presents the profile of the four discrepancy segments in terms of the
percentage of gamblers in those segments having indications on each of the ten
FLAGS-EGM constructs, as well as the percentage of those gamblers responding
either sometimes or more often to each of the nine PGSI statements.
Discussion
PLS-SEM was used successfully to create a model to identify risk for problem
gambling and to classify an individual as a Problem Gambler. The model utilized all
ten constructs developed for this purpose and passed the four tests specified by
Table 7
Overlap in Classification by Risk Categories Between FLAGS-EGM and PGSI
PGSI
Categories
FLAGS
Categories
No Detectable
Risk
Early
Risk
Intermediate
Risk
Advanced
Risk
Problem
Gambler
Total
No Risk 47.1% 6.4% 0.8% 0.5% 0.0% 54.8%
176 24 3 2 0 205
Low Risk 11.2% 2.4% 4.3% 1.1% 0.3% 19.3%
42 9 16 4 1 72
Medium Risk 2.1% 1.6% 6.7% 7.2% 2.7% 20.3%
8 6 25 27 10 76
Problem
Gambler
0.0% 0.0% 0.0% 0.8% 4.8% 5.6%
0 0 0 3 18 21
Total 60.4% 10.4% 11.8% 9.6% 7.8% 100.0%
226 39 44 36 29 374
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
Table 8
Comparisons of Discrepancy Segments
PGSI At
Risk –
FLAGS-
EGM
No-Risk
FLAGS-
EGM At
Risk –PGSI
No-Risk
PGSI Low
Risk –
FLAGS-
EGM Higher
Risk
FLAGS-
EGM
PG –PGSI
Medium
Risk
FLAGS (% flagged on construct) (n= 50) (n= 29) (n= 21) (n= 10)
Risky Cognitions Beliefs 0.0% 44.8% 0.0% 10.0%
Risky Cognitions Motives 0.0% 37.9% 33.3% 80.0%
Preoccupation Desire 0.0% 24.1% 42.9% 30.0%
Risky Behaviours Earlier 0.0% 10.3% 42.9% 80.0%
Impaired Control Continue 0.0% 17.2% 57.1% 100.0%
Impaired Control Begin 0.0% 3.4% 0.0% 40.0%
Risky Behaviours Later 0.0% 3.4% 23.8% 60.0%
Preoccupation Obsession 0.0% 0.0% 0.0% 20.0%
Negative Consequences 0.0% 6.9% 0.0% 100.0%
Persistence 0.0% 6.9% 4.8% 100.0%
PGSI (% responded sometimes or more
often)
You bet more on the slot machines at a
casino than you could really afford to
lose?
54.0% 0.0% 42.9% 90.0%
You needed to gamble on the slot
machines at a casino with larger
amounts of money to get the same
feeling of excitement?
14.0% 0.0% 19.0% 30.0%
When you gambled on the slot machines
at a casino, you went back another day
to try and win back the money you lost?
38.0% 0.0% 33.3% 60.0%
You borrowed money or sold anything to
get money to gamble on the slot
machines at a casino?
2.0% 0.0% 4.8% 30.0%
You felt that you might have a problem
with gambling on the slot machines at a
casino?
10.0% 0.0% 23.8% 90.0%
People have criticized your betting or told
you that you had a gambling problem
with slot machines at a casino,
regardless of whether or not you
thought it was true?
14.0% 0.0% 14.3% 50.0%
You have felt guilty about the way you
gamble, or what happens when you
gamble on the slot machines at a casino?
24.0% 0.0% 14.3% 80.0%
Your gambling on slot machines at a
casino has caused you any health
problems, including stress or anxiety?
2.0% 0.0% 0.0% 10.0%
Your gambling on slot machines at a
casino has caused any financial
problems for you or your household?
0.0% 0.0% 0.0% 40.0%
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
Urbach and Ahlemann (2010). Specifically, the coefficients of determination were
sufficient, the path coefficients were significant, the independent latent variables had
medium impact on dependent latent variables, and all constructs had a predictive
relevance greater than 0.0. In addition, ROC analyses identified the optimal cut off
to form indicators of risk that met our criteria. As a result of these analyses, gamblers
were classified as being in one of five categories as described in detail immediately
below.
Problem Gamblers
Indications of both Negative Consequences and Persistence were required before an
individual was considered to be a Problem Gambler. It should be noted that in
creating the Negative Consequence construct, we did not address all possible forms
of harm. We did not ask questions about aggressive or illegal behaviours,
relationship problems, mental illness or attempted suicide. We hypothesized that
individuals suffering these more severe consequences of gambling would also ‘‘ flag’’
on the less severe and more generally-phrased statements. Moreover, queries of this
nature could be viewed as threatening and therefore left unanswered by some
respondents or lead others to stop participating in the survey altogether. Researchers
who want to know the prevalence of these more severe consequences could include
additional questions but would need to exclude them when deriving the Negative
Consequences indicator.
The FLAGS-EGM instrument categorized 7.8% of the individuals as problem
gamblers compared with 5.6% as identified by the PGSI for the same sample.
Without further research, it cannot be determined if the FLAGS-EGM would always
have a higher identification rate. It may be that the fourteen negative-consequence
statements triggered recognition on the part of respondents as to the harms they had
experienced, thereby identifying gamblers who would not in fact be classified as
problem gamblers using the PGSI.
Advanced Risk
Individuals with Advanced Risk could have indications of either Persistence or
Negative Consequences in contrast with Problem Gamblers who displayed both
characteristics. Preoccupation Obsession, Risky Practices Later and Impaired
Control Begin were also associated with Advanced Risk (Table 6). A third of the
Advanced Risk group also had an indication of Persistence. This finding is an
important indicator of risk in this group as it identifies those respondents who admit
they intend to continue gambling despite the fact that it will lead to further harms.
Although 22.2% of those designated as being at Advanced Risk had an indication of
Negative Consequences, they were not in fact persisting in gambling. There are
several possible explanations for this. Some gamblers, once they had experienced
harms, may in turn have found ways to control their gambling behaviour. Others
may have stopped gambling within the one-year time frame designated in the
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
instrument and thus did not have an indication of persistence. Still others may have
only recently experienced harms and had yet to become persistent in their gambling
behaviour. Regardless, however, of their respective particular situations, these
gamblers were nonetheless placed in the Advanced
Risk category because they experienced Negative Consequences. The mere
possibility of their relapsing or persisting in gambling was sufficient to warrant
assigning them to this category.
Impaired Control Begin influenced Risky Practices Later and also had a direct and
fairly strong impact on Negative Consequences (Fig. 1). The Responsible Gaming
Device (RGD) and RG Tracking System, designed by Techlink Entertainment
Systems and tested in Windsor, Nova Scotia (Schellinck & Schrans, 20067), includes
built-in self-exclusion features, as well as an option to track expenditures over
extended periods; this specific option could help a gambler overcome Impaired
Control Begin. This feature would also be particularly useful where—via wide-area
networks in smaller venues, such as bars and clubs—EGMs are provided. Assistance
delivered by Gamblers Anonymous and counsellors, as well as venue exclusion
programs, are specifically designed to work with individuals with this degree of
impaired control.
Risky Practices Later also had a strong connection to Negative Consequences
(Fig. 1). By limiting the amount of money that can be borrowed on the premises, and
by ensuring that loan sharks are kept away, the operators or staff of venues could be
of help to gamblers who are attempting to borrow money. Pre-commitment could be
effective in assisting such individuals reduce these highly risky practices.
Intermediate Risk
The reflective construct Impaired Control Continue and the formative construct
Risky Practices Earlier were the indicators of Intermediate Risk (Table 6). Impaired
Control Continue, in turn, had a strong influence on Risky Practices Earlier and
Impaired Control Begin (Fig. 1). This latter relationship suggests that gamblers first
lose control during a session, and then later lose control between sessions because of
an inability to resist gambling again.
Both Impaired Control Continue and Risky Practices Earlier were mainly associated
with behaviours that would occur ‘‘ on the floor,’’ and which gamblers themselves
could potentially cut back on. Consequently, interacting with individuals on location
during a gambling session could be important in reducing their risk levels. These
gamblers would most likely benefit from responsible gambling features such as the
Live Action component of the My Play system (Schellinck & Schrans, 2007;
Schellinck & Schrans, 2011). This feature provides a gambler with real-time
monitoring of gambling activities, including cumulative spending during the session.
Such a finding provides support for a role of My Play or a similar program to help
individuals control their risky behaviours once identified.
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
Gamblers who were administered the FLAGS-EGM instrument have been shown to
be motivated to control their gambling (Buckley, 2013). Providing gamblers access to
the FLAGS-EGM while on the floor, either in the form of a pamphlet (Buckley,
2013) or on screen through the EGM interface, could be helpful in reducing risky
practices in the casino.
Early Risk
A respondent needed to endorse statements associated with Risky Cognitions
Motives, Risky Cognitions Beliefs or Preoccupation Desire to be classified as an
Early Risk gambler. A relatively large proportion of the entire sample, i.e., 17.1%,
had, regardless of their level of risk, an indication of Risky Cognitions Motives. In
contrast, Risky Cognitions Beliefs appeared to be a risk factor early for certain
gamblers (Fig. 1). Risky Cognitions Motives, with the largest number of paths
leading from it in the PLS-SEM model influenced Preoccupation Desire,
Preoccupation Obsession, Risky Behaviour Earlier and Risky Behaviour Later,
and was the third most common indicator for the Problem Gamblers. These results
suggest that it may be more effective to lower an individual’s risk by reducing risky
motives than by reducing risky beliefs. Moreover, these results emphasize the need
for investigation into the factors underlying motives in the effort to decrease risk due
to gambling.
Preoccupation Desire, experienced by 62.1% of the Problem Gamblers, had a very
strong influence on Impaired Control Continue, the most common indicator for risk.
Reducing the entertainment value of the gambling experience, changing the
reinforcement schedule by changing the frequency and nature of wins, or reducing
the marketing material received by the gambler, might each in turn affect the
gambler’s desire to gamble. Gamblers have little control over these influences, and
changes to these elements that might reduce Preoccupation Desire will likely need to
be initiated by the gambling providers and regulators.
No Detectable Risk
For gamblers to be placed in the No Detectable Risk category, FLAGS-EGM would
not have given any indications of risk or harms because of gambling within the last
year. Gamblers may have endorsed a few of the statements but not enough of them
within a construct to provide sufficient evidence for an indication of risk on that
criterion. Certain of these gamblers may indeed have been at risk or problem
gamblers in the past but at least for the previous year they are not in these categories.
Certain of these gamblers will also have personal or situational factors that could
lead to higher risk levels associated with gambling, levels that we have not measured.
Nonetheless, these factors have as yet not manifested themselves in terms of
cognitions about gambling, impaired control, preoccupation, risky practices or
harms and persistence, and therefore are likely to remain at that risk level until
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
something in their environment triggers changes that lead to elevated risk levels. This
instrument would at that time identify these persons.
Comparison of the FLAGS-EGM to the PGSI
Overall, the sizes of the risk segments created by FLAGS-EGM and the PGSI are
similar. However, the number of Problem Gamblers identified by the FLAGS-EGM is
somewhat larger. The two instruments differed markedly in terms of assigning individuals
to specific levels of risk. In fact, they agreed in only about one-third of the cases. As well,
one-fifth of those in the sample were assigned to a risk category by just one of the
instruments. The profile of four discrepancy segments (Table 8) provides more insight as
to why the two instruments classified gamblers into different risk categories. As discussed
below, these results suggest FLAGS-EGM is a more appropriate instrument for
identifying and categorizing gamblers at risk of becoming a Problem Gambler.
Segment One. Segment One comprised those individuals who were categorized
as at risk (Low Risk and Medium Risk) by the PGSI and No Detectable Risk by the
FLAGS-EGM. These persons may rarely exhibit the characteristics identified in the
PGSI as 94% of the responses by those in this segment were ‘‘ Sometimes.’’ The single
largest contributor (54.2%) to designating these persons as at risk by the PGSI is the
statement ‘‘ you bet more than you could really afford to lose.’’ In a casino
environment overspending may occur ‘‘ sometimes’’ for a variety of reasons unrelated
to risk. For example, friends are not ready to leave, the bus is not ready to go, a
friend is winning or the environment is exciting. FLAGS-EGM has a similar
statement but it does not categorize gamblers, based on this statement alone, as at
risk. The PGSI also classifies gamblers as at risk if they ‘‘ sometimes’’ feel guilty
about their gambling; a number of gamblers (24%) in this segment indicated that
they sometimes feel as this. These feelings of guilt may be mediated by just being
present in the casino environment regardless of one’s gambling behaviour.
Individuals are cautioned constantly to ‘‘ play within their limits,’’ or ‘‘ gamble
responsibly.’’ This reminder could lead them to agree that they sometimes feel guilty
about their gambling.
The statement in the PGSI ‘‘ went back another day to try and win back the money
you had lost’’ is meant to identify chasing behaviour. However, as worded, it may
have led individuals to endorse this behaviour and thus erroneously led to the PGSI
classifying gamblers as Low Risk based on behaviour that is not in fact really chasing
behaviour. For example, gamblers who (1) visit the casino more frequently and thus
gamble more often in consecutive days, or (2) regularly gamble over two days of the
weekend, or (3) go to a casino as a destination, and therefore play for several days in
a row, are, in all three cases, more likely to answer ‘‘ sometimes’’ to this statement.
When asked about their intentions when returning to gamble, such gamblers may
admit to wanting ‘‘ sometimes’’ to win back the money they had previous lost even
though the behaviour was not problematic. To reduce such a frequency bias, the
equivalent FLAGS-EGM statement included the phrase ‘‘ after losing more money
than I wanted.’’ The FLAGS-EGM also addressed and clarified the more specific
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
aspects of the situation (‘‘ I usually try to win it back by playing again either later that
day or on another day’’ ). In general, gamblers classified as Low Risk by the PGSI
but at No Detectable Risk by FLAGS-EGM have answered ‘‘ sometimes’’ to
questions that may have relatively low thresholds for casino gamblers. Using such a
statement could inappropriately classify someone as having a risk for problem
gambling based on such a criterion.
Additionally, individuals categorized as Low Risk by the PGSI but as No Detectable
Risk by FLAGS-EGM were only required to answer ‘‘ sometimes’’ to one question on
the PGSI to be identified as Low Risk. These individuals made up approximately two-
thirds (63.9%) of PGSI Low Risk Gamblers in this sample and nearly a third (31.1%) of
all of those identified by the PGSI as at risk. The PGSI and other instruments that rely
on a continuum based on a sum score starting at 1 are effectively categorizing persons
based on the endorsement of a single statement. In contrast, individuals that endorsed no
more than one statement in all the constructs in FLAGS-EGM were assigned to the No
Detectable Risk category. In line with the arguments for using multi-item measures to
identify latent constructs (Churchill, 1979) a minimum score of 2—i.e., endorsement of
two questions or more—wasneededtosaywith confidence that a person holds an
indication of risk on any one of the ten constructs in the FLAGS-EGM.
Segment Two. This group comprised those gamblers not designated by the
PGSI as at risk but designated by FLAGS-EGM as Early Risk. Those gamblers who
flagged on any of three indicators, Risky Cognitions Beliefs, Risky Cognitions
Motives and Preoccupation Desire (Table 8), but did not flag on the more advanced
indicators of risk, were designated as Early Risk gamblers. The PGSI does not have
statements that cover these indicators. However, the development of the FLAGS-
EGM constructs was based on an extensive review of the literature as well as
previous research with samples of the gambling population that identified these
indicators as risk factors to test and our PLS-SEM analysis showed them to be
significantly related to the development of problem gambling. This finding suggests
that administering this instrument could provide valuable predictors of individuals at
risk for problem gambling that are not currently being assessed by the PGSI.
Segment Three. These gamblers were classified as Low Risk by the PGSI but
are classified as Intermediate or Advanced Risk gamblers by the FLAGS-EGM. The
concerns and potential bias associated with the statements in the PGSI, and the
alternative approach used in the FLAGS-EGM, can be found in the discussion on
Segment One and will not be further described here.
In this case, because most of the responses to the PGSI statements are ‘‘ sometimes,’’
the total PGSI score does not exceed 2 for these gamblers, placing them in the Low
Risk category. In FLAGS-EGM, sometimes exhibiting higher-risk characteristics is
deemed to be sufficient reason to place them in a higher risk category. The point is
this. By not treating all statements equally in terms of risk indication—i.e., the words
‘‘ sometimes,’’ ‘‘ often’’ and ‘‘ frequently’’ are used in the statements to weight
effectively the statement itself, and the statements are placed in constructs that are
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
associated with different risk levels—theFLAGS-EGMcanbetterassignapersontoa
risk category, based on both the extent and the riskiness of the behaviour. This feature
means that the gambler, to have an indication of Intermediate Risk, often needs to spend
more time gambling than intended but only needs sometimes to borrow money from
other gamblers to be classified as an indication of Advanced Risk.
Segment Four. Gamblers who were classified as Medium Risk by the PGSI and
Problem Gamblers by the FLAGS-EGM were included in this category. Almost all
of these gamblers (90%) indicated through the PGSI that they believed they had a
gambling problem ‘‘ sometimes’’ but this was not enough to categorize them as
Problem Gamblers. The difference is because all participants identified as Problem
Gamblers in the FLAGS-EGM indicated experiencing at least three harms caused by
gambling and exhibited Persistence. In the PGSI, if we assume the last four
statements all have to do with negative consequences then the respondents could say
they sometimes experience these consequences, but this is not sufficient to move them
into the problem gambling category. In the FLAGS-EGM for example, having had
problems paying off debts in the last year at all, or sometimes having to juggle money
and bills in order to gamble, are indications of negative consequences that led them
to be classified as Problem Gamblers. These gamblers indicated experiencing three or
more of the fourteen consequences listed in FLAGS-EGM and therefore met the
Negative Consequences criterion for Problem Gambler. The main consequences
indicated were: (1) They do not want others to know about their gambling
behaviours; (2) they feel depressed about their gambling; (3) they believe gambling
has interfered with their life’s goals, and (4) they do not like the type of person they
have become. This finding suggests that the PGSI may not be identifying negative
consequences that are related to the gambler’s self-perception and state of mind.
A key difference between the FLAGS-EGM classification scheme and that of other
instruments such as the PGSI and SOGS is that with the FLAGS-EGM the gambler
is classified based on the nature of the indicators flagged. With the former screens,
the gambler is classified based on a summed score. Thus, individuals who indicated
that they sometimes borrow money to gamble could be placed at a low risk level by
the PGSI (depending upon what other statements they endorsed). In comparison, the
FLAGS-EGM would consider borrowing money a high-risk behaviour that
consequently identified a gambler as Advanced Risk. We believe this method of
classification is a major strength of the FLAGS-EGM instrument.
Limitations
Our analysis provides strong evidence that people will progress towards problem
gambling if they have these indicators as described in this study. To test this
hypothesis further, we need a longitudinal study to measure the movement of
gamblers between risk groups over time. Of course, this research is based on a
convenience sample of Ontario slot players, and further research is consequently
needed to determine if the relationships identified here exist in other jurisdictions.
Furthermore, our research applies only to EGM players. When developed in
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
Schellinck, T. et al. (in press), the Preoccupation Obsession construct passed all
validity and reliability tests, except for Composite Reliability, which requires at least
three items in the construct to produce an accurate statistic. We therefore used the
two statement version of the construct in this phase of the analysis in order to
construct the FLAGS-EGM instrument. Further research is being conducted with a
revised version of the construct containing four items.
It is often suggested that when conducting the ROC analysis the state variable should
be a ‘‘ gold standard.’’ When conducting ROC analysis on the Negative Consequences
and Persistence constructs we used the PGSI score of 8+as our state variable.
However, we are not aware of any validated risk measures that could be used as gold
standards, and therefore, as state variables for the constructs in each preceding risk
level, had to rely on the risk level indicators already created using ROC analysis within
FLAGS-EGM. Thus, those gamblers already classified as Intermediate Risk were the
state used in the ROC analysis when analysing the three Early Risk constructs, and the
sample size of 309 was more than sufficient for this purpose.
The instrument was designed to identify at risk and Problem Gamblers who are at
risk due to EGM gambling. As such, it was done to make the terms in the statements
more exact and therefore the instrument more accurate and easier to self-administer.
This fact means it cannot be used to measure risk due to other forms of gambling.
However, in many jurisdictions that offer wide-area network gambling, EGM
gambling is the primary form of gambling, and thus needs to be studied separately.
Our experience is that regulators, whose jurisdiction includes a large number of
EGMs, as well as gambling providers such as casinos and betting shops, want to
measure carefully the impact of EGMs on risk and Problem Gambling exclusive of
table games, sports betting and lotteries, and as such the FLAGS-EGM will find
many applications.
Conclusions
Using SEM-PLS analysis, we have created an instrument that should provide reliable
information to EGM gamblers concerning their risk levels. As summarized below,
the FLAGS-EGM has several key characteristics that make it very suitable for use as
a measure of gambling risk and harm.
First, based upon its design, the instrument should be highly effective in identifying
individuals at risk due to EGM gambling. We have chosen indicators that are proven
to be associated with problem gambling (beliefs, motives, impaired control,
preoccupation, consequences and persistence). Moreover, the individual must be
an active gambler to respond to the statements.
Second, the FLAGS-EGM is easily administered, either by gamblers, themselves, or
in a clinical context. Gamblers understood the statements, interpreted them
consistently and believed that they were relevant to their situation (Schellinck,
T. et al., in press). Consequently, both gamblers and health providers should be able
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INSTRUMENT DEVELOPMENT FOR THE FLAGS-EGM
to assess in an accurate and informative manner individual risk levels caused by
gambling.
Third, the instrument could be set up as a responsible gambling (RG) module on
gambling machines or players could be invited to fill out the FLAGS-EGM on the
Internet at an RG site. When administered via computer the number and nature of
risk indicators, and the level of their risk associated with gambling, could be
provided automatically to the gambler. Fourth, and perhaps most important,
the instrument could provide policy makers with detailed information as to the
nature of risk faced by gamblers. Using the FLAGS-EGM in this manner could lead
to effective solutions for reducing the potential for the harms associated with
gambling.
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Appendix
FLAGS-EGM (Beta)
Risky Cognitions Beliefs
You can sometimes tell when the machine is about to pay out big because the
symbols start getting closer to lining up on the pay line (e.g., almost winning).
I feel the machines are fixed sometimes so that you can’t win on them.
It is important for me to use a system or a strategy when I play the machines.
I believe that in the long run I can win playing slots at the casino.
If a slot machine hasn’t had a big pay out in a long time, it is more likely to do so
soon.
Risky Cognitions Motives
I sometimes play the slots in hopes of paying off my debts/bills.
I sometimes play the slots when I’m feeling down or depressed.
Gambling on the slots is a way I can try to get some money when I need it.
can escape by playing the slots whenever I am worried or under stress.
Preoccupation Desire
If I could play the machines all the time I would.
I wish I could gamble on the slots more often.
I would like to play the slots almost every day.
I like to play the slot machines every chance I get.
Preoccupation Obsession
I sometimes dream about playing the slot machines.
I spend more time than I used to thinking about playing the slots.
Risky Practices Earlier
I sometimes exceed the amount of money I intended to spend in order to win back
money I have lost.
When gambling on the slots I usually use my bank or debit card to get more money
so I can keep playing.
I play max bet if I’m on a winning streak.
If I win big I am likely to put the money back into a machine and keep playing.
When gambling on a slot machine I usually play as fast as I can.
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I have sometimes gambled for more than six hours straight when I was playing the
slots.
Risky Practices Later
After losing more money than I wanted on the slots I usually try to win it back by
playing again either later that day or on another day.
When gambling on the slots I usually use my credit card to get more money so I
can keep playing.
When I gamble with friends or family I sometimes stay and continue to play after
they have stopped or left.
I have sometimes borrowed money so I could go and gamble on the slots.
I have borrowed money from other people at the casino in order to continue
gambling.
I have left the casino to get more money so I can come back and keep on gambling.
Impaired Control Continue
I often spend more money gambling than I intended.
Even when I intend to spend a few dollars gambling, I often end up spending much
more.
I sometimes gamble with money that I can’t really afford to lose.
Once I have started gambling on the slots I find it very hard to stop.
I often spend more time gambling than I intend to.
Impaired Control Begin
I have tried to cut back on my slots play with little success.
I have tried unsuccessfully to stop or reduce my gambling on the slots.
There have been times I have gambled despite my desire not to.
Negative Consequences
My goals in life have been jeopardized by my slot play.
I often can’t sleep because I am worrying about my slot machine gambling.
I have had problems paying off debts accumulated from playing the slots.
Since I started playing the slots I don’t like the type of person I have become.
Sometimes I have to juggle money and bills to cover the cost of my slot machine
gambling.
I wouldn’t want anyone to know how much time or money I spend at the casino.
Sometimes I feel depressed over my slots play.
Others are disappointed in me because of my gambling.
I have friends or family who are concerned about my slots play.
I have sometimes missed events or neglected family, friends or work in order to
play the slots.
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When I leave the casino, I have sometimes been short of cash for parking, food, or
a ride home.
I have become somewhat of a loner because of my slot gambling.
I sometimes have spent time gambling on the slots when I was supposed to be
doing something else important.
My gambling has caused me to have a falling out with the people I used to hang
out with.
Persistence
I continue to play the machines despite experiencing problems or other negative
consequences.
I continue to gamble despite the bad things that happen to me.
I gamble even though I know it is likely to lead to problems for me.
Even if money is tight, I continue to play the slots to get big wins.
*******
Manuscript history: Submitted October 16, 2012; Accepted October 27, 2014.
For correspondence: Tony Schellinck, PhD, Focal Research Consultants Limited, 7071
Bayers Rd., Suite 326, Halifax, NS B3L 2C2, E-mail: tschellinck@focalresearch.com,
Website address: http://www.focalresearch.com
Competing interests: None declared.
Ethics approval: The Ontario Institutional Review Board (ON IRB). Final protocol
approval was obtained for ‘‘ Preliminary Development of a Self Administered
Gambling Risk Assessment Instrument for Slots’’ on June 23 2008.
Funding: Ontario Problem Gambling Research Centre: Grant # 2755.
Contributors: T. Schellinck planned the document. T. Schellinck and HS drafted and
wrote the manuscript with editorial contributions from T. Schrans and MB. HS and
T. Schrans conducted the gambling-related literature review. T. Schrans conceptua-
lized the research design and conducted the focus group and survey studies. T.
Schellinck and MB assessed the current analytical literature and designed the
analysis approach. T. Schellinck conducted the analysis and finalized the design of
the constructs.
Dr. Tony Schellinck is an Adjunct Professor in the Faculty of Graduate Studies and
the Rowe School of Business at Dalhousie University, Canada, as well as CEO of
Focal Research Consultants Limited. From 1996 to 2013 he was the F. C. Manning
Chair in Economics and Business at Dalhousie University. Since 1989 he has
conducted research into gambling behaviour for industry, government, public health
and regulatory agencies. This work included a ten-year large-scale monthly tracking
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study of gambling behaviour, over 300 focus group sessions with gamblers, the 1998
Nova Scotia Video Lottery Study, two large scale studies into the value of
responsible gambling features on VLT machines, and the Nova Scotia Adolescent
Gambling Exploratory Research: Identification of Risk and Gambling Harms
Among Youth. Dr. Schellinck worked on creating the first algorithms deployed in
casinos that identified using player loyalty data high-risk gamblers.
Ms. Tracy Schrans is Principal and President of Focal Research Consultants an
independent research firm in Halifax, NS. Over the last twenty years Tracy has
conducted numerous government, public health, and industry-sponsored research
projects on a wide range of issues, with a particular emphasis on gambling- and
alcohol-related issues. She consults internationally in responsible gambling and
corporate social responsibility, social policy, player tracking and loyalty data
analysis. Ms. Schrans is one of the developers of new instruments for measuring pre-
harm risk for gambling among adults (FLAGS-EGM and FLAGS General) and
adolescents (FYGRS) for prevention applications. She continues to work at the
forefront of gambling behavior analytics, assisting gambling stakeholders in using
system data, measurement, and technology to help identify, manage and prevent
gambling risk and harm among their customers.
Dr. Schellinck, PhD, is an Adjunct Professor in the Faculty of Graduate Studies and
Department of Psychology and Neuroscience at Dalhousie University. Her research
is primarily focused on learning and memory in animal models of neurodegenerative
disease.
Dr. Michael Bliemel is an associate professor of Management Information Systems
at Dalhousie University in Halifax, NS. He completed his PhD at McMaster
University in Management Science/Systems, specializing in the quantitative
modeling of consumer behaviour with health information systems. His current
research interests include the strategic management of information systems and
innovation in organizations, and business intelligence applications.
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